# BSD 3-Clause License
#
# Copyright (c) 2017 xxxx
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# Copyright 2021 Huawei Technologies Co., Ltd
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# modification, are permitted provided that the following conditions are met:
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# * Redistributions of source code must retain the above copyright notice, this
#   list of conditions and the following disclaimer.
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#   this list of conditions and the following disclaimer in the documentation
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#   contributors may be used to endorse or promote products derived from
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# ============================================================================
import math

import torch
import torch.nn as nn
import torch.nn.functional as F


def default_conv(in_channels, out_channels, kernel_size, bias=True):
    return nn.Conv2d(
        in_channels, out_channels, kernel_size, padding=(kernel_size // 2), bias=bias
    )


class MeanShift(nn.Conv2d):
    def __init__(
        self,
        rgb_range,
        rgb_mean=(0.4488, 0.4371, 0.4040),
        rgb_std=(1.0, 1.0, 1.0),
        sign=-1,
    ):

        super(MeanShift, self).__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std
        for p in self.parameters():
            p.requires_grad = False


class BasicBlock(nn.Sequential):
    def __init__(
        self,
        conv,
        in_channels,
        out_channels,
        kernel_size,
        bias=False,
        bn=True,
        act=nn.ReLU(True),
    ):

        m = [conv(in_channels, out_channels, kernel_size, bias=bias)]
        if bn:
            m.append(nn.BatchNorm2d(out_channels))
        if act is not None:
            m.append(act)

        super(BasicBlock, self).__init__(*m)


class ResBlock(nn.Module):
    def __init__(
        self,
        conv,
        n_feats,
        kernel_size,
        bias=True,
        bn=False,
        act=nn.ReLU(True),
        res_scale=1,
    ):

        super(ResBlock, self).__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x).mul(self.res_scale)
        res += x

        return res


class Upsampler(nn.Sequential):
    def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True):

        m = []
        if (scale & (scale - 1)) == 0:  # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feats, 4 * n_feats, 3, bias))
                m.append(nn.PixelShuffle(2))
                if bn:
                    m.append(nn.BatchNorm2d(n_feats))
                if act == "relu":
                    m.append(nn.ReLU(True))
                elif act == "prelu":
                    m.append(nn.PReLU(n_feats))

        elif scale == 3:
            m.append(conv(n_feats, 9 * n_feats, 3, bias))
            m.append(nn.PixelShuffle(3))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if act == "relu":
                m.append(nn.ReLU(True))
            elif act == "prelu":
                m.append(nn.PReLU(n_feats))
        else:
            raise NotImplementedError

        super(Upsampler, self).__init__(*m)
